Overview

Dataset statistics

Number of variables20
Number of observations9725
Missing cells9676
Missing cells (%)5.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory1.4 MiB
Average record size in memory153.0 B

Variable types

Numeric14
Categorical5
Boolean1

Warnings

over_under_line has a high cardinality: 68 distinct values High cardinality
elo1_pre is highly correlated with qbelo1_preHigh correlation
elo2_pre is highly correlated with qbelo2_preHigh correlation
elo_prob1 is highly correlated with elo_prob2 and 2 other fieldsHigh correlation
elo_prob2 is highly correlated with elo_prob1 and 2 other fieldsHigh correlation
qbelo1_pre is highly correlated with elo1_preHigh correlation
qbelo2_pre is highly correlated with elo2_preHigh correlation
qbelo_prob1 is highly correlated with elo_prob1 and 2 other fieldsHigh correlation
qbelo_prob2 is highly correlated with elo_prob1 and 2 other fieldsHigh correlation
stadium_neutral is highly correlated with compass_homeHigh correlation
compass_home is highly correlated with stadium_neutralHigh correlation
compass_home has 9676 (99.5%) missing values Missing
dt_for_home is highly skewed (γ1 = 21.07824614) Skewed
df_index is uniformly distributed Uniform
df_index has unique values Unique
qbelo1_pre has unique values Unique
qbelo2_pre has unique values Unique
qbelo_prob1 has unique values Unique
qbelo_prob2 has unique values Unique
spread_favorite has 133 (1.4%) zeros Zeros
dt_for_home has 9676 (99.5%) zeros Zeros

Reproduction

Analysis started2021-04-16 16:49:53.648888
Analysis finished2021-04-16 16:50:15.882873
Duration22.23 seconds
Software versionpandas-profiling v2.11.0
Download configurationconfig.yaml

Variables

df_index
Real number (ℝ≥0)

UNIFORM
UNIQUE

Distinct9725
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7160
Minimum2298
Maximum12022
Zeros0
Zeros (%)0.0%
Memory size76.1 KiB
2021-04-16T12:50:15.952404image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/

Quantile statistics

Minimum2298
5-th percentile2784.2
Q14729
median7160
Q39591
95-th percentile11535.8
Maximum12022
Range9724
Interquartile range (IQR)4862

Descriptive statistics

Standard deviation2807.510018
Coefficient of variation (CV)0.3921103377
Kurtosis-1.2
Mean7160
Median Absolute Deviation (MAD)2431
Skewness0
Sum69631000
Variance7882112.5
MonotocityStrictly increasing
2021-04-16T12:50:16.086534image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
40981
 
< 0.1%
27401
 
< 0.1%
47911
 
< 0.1%
109361
 
< 0.1%
88891
 
< 0.1%
27481
 
< 0.1%
68461
 
< 0.1%
47991
 
< 0.1%
109441
 
< 0.1%
88971
 
< 0.1%
Other values (9715)9715
99.9%
ValueCountFrequency (%)
22981
< 0.1%
22991
< 0.1%
23001
< 0.1%
23011
< 0.1%
23021
< 0.1%
ValueCountFrequency (%)
120221
< 0.1%
120211
< 0.1%
120201
< 0.1%
120191
< 0.1%
120181
< 0.1%

result
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size76.1 KiB
1
5637 
0
4088 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters9725
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row0
3rd row1
4th row1
5th row1
ValueCountFrequency (%)
15637
58.0%
04088
42.0%
2021-04-16T12:50:16.297416image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram of lengths of the category
2021-04-16T12:50:16.355266image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
ValueCountFrequency (%)
15637
58.0%
04088
42.0%

Most occurring characters

ValueCountFrequency (%)
15637
58.0%
04088
42.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number9725
100.0%

Most frequent character per category

ValueCountFrequency (%)
15637
58.0%
04088
42.0%

Most occurring scripts

ValueCountFrequency (%)
Common9725
100.0%

Most frequent character per script

ValueCountFrequency (%)
15637
58.0%
04088
42.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII9725
100.0%

Most frequent character per block

ValueCountFrequency (%)
15637
58.0%
04088
42.0%

spread_favorite
Real number (ℝ)

ZEROS

Distinct47
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-5.394190231
Minimum-26.5
Maximum0
Zeros133
Zeros (%)1.4%
Memory size76.1 KiB
2021-04-16T12:50:16.430511image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/

Quantile statistics

Minimum-26.5
5-th percentile-12.5
Q1-7
median-4.5
Q3-3
95-th percentile-1
Maximum0
Range26.5
Interquartile range (IQR)4

Descriptive statistics

Standard deviation3.430153417
Coefficient of variation (CV)-0.6358977473
Kurtosis1.163035489
Mean-5.394190231
Median Absolute Deviation (MAD)2
Skewness-1.066664965
Sum-52458.5
Variance11.76595246
MonotocityNot monotonic
2021-04-16T12:50:16.560630image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram with fixed size bins (bins=47)
ValueCountFrequency (%)
-31492
15.3%
-3.5763
 
7.8%
-7701
 
7.2%
-2.5626
 
6.4%
-6532
 
5.5%
-4530
 
5.4%
-6.5489
 
5.0%
-1472
 
4.9%
-2421
 
4.3%
-4.5342
 
3.5%
Other values (37)3357
34.5%
ValueCountFrequency (%)
-26.51
< 0.1%
-24.51
< 0.1%
-241
< 0.1%
-22.51
< 0.1%
-21.51
< 0.1%
ValueCountFrequency (%)
0133
 
1.4%
-1472
4.9%
-1.5302
3.1%
-2421
4.3%
-2.5626
6.4%

over_under_line
Categorical

HIGH CARDINALITY

Distinct68
Distinct (%)0.7%
Missing0
Missing (%)0.0%
Memory size76.1 KiB
41
 
564
44
 
511
42
 
507
43
 
487
37
 
474
Other values (63)
7182 

Length

Max length4
Median length2
Mean length2.692133676
Min length1

Characters and Unicode

Total characters26181
Distinct characters12
Distinct categories3 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique6 ?
Unique (%)0.1%

Sample

1st row30
2nd row39
3rd row31
4th row31.5
5th row37
ValueCountFrequency (%)
41564
 
5.8%
44511
 
5.3%
42507
 
5.2%
43487
 
5.0%
37474
 
4.9%
40470
 
4.8%
38451
 
4.6%
39425
 
4.4%
45410
 
4.2%
43.5309
 
3.2%
Other values (58)5117
52.6%
2021-04-16T12:50:16.822219image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
41564
 
5.8%
44511
 
5.3%
42507
 
5.2%
43487
 
5.0%
37474
 
4.9%
40470
 
4.9%
38451
 
4.7%
39425
 
4.4%
45410
 
4.2%
43.5309
 
3.2%
Other values (57)5062
52.3%

Most occurring characters

ValueCountFrequency (%)
46952
26.6%
54941
18.9%
34136
15.8%
.3393
13.0%
71156
 
4.4%
8993
 
3.8%
1989
 
3.8%
6975
 
3.7%
2889
 
3.4%
0860
 
3.3%
Other values (2)897
 
3.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number22733
86.8%
Other Punctuation3393
 
13.0%
Space Separator55
 
0.2%

Most frequent character per category

ValueCountFrequency (%)
46952
30.6%
54941
21.7%
34136
18.2%
71156
 
5.1%
8993
 
4.4%
1989
 
4.4%
6975
 
4.3%
2889
 
3.9%
0860
 
3.8%
9842
 
3.7%
ValueCountFrequency (%)
.3393
100.0%
ValueCountFrequency (%)
55
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common26181
100.0%

Most frequent character per script

ValueCountFrequency (%)
46952
26.6%
54941
18.9%
34136
15.8%
.3393
13.0%
71156
 
4.4%
8993
 
3.8%
1989
 
3.8%
6975
 
3.7%
2889
 
3.4%
0860
 
3.3%
Other values (2)897
 
3.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII26181
100.0%

Most frequent character per block

ValueCountFrequency (%)
46952
26.6%
54941
18.9%
34136
15.8%
.3393
13.0%
71156
 
4.4%
8993
 
3.8%
1989
 
3.8%
6975
 
3.7%
2889
 
3.4%
0860
 
3.3%
Other values (2)897
 
3.4%

stadium_neutral
Boolean

HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size9.6 KiB
False
9676 
True
 
49
ValueCountFrequency (%)
False9676
99.5%
True49
 
0.5%
2021-04-16T12:50:16.888461image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/

dt_for_home
Real number (ℝ≥0)

SKEWED
ZEROS

Distinct36
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean11.60972704
Minimum0
Maximum5448.926695
Zeros9676
Zeros (%)99.5%
Memory size76.1 KiB
2021-04-16T12:50:16.956606image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum5448.926695
Range5448.926695
Interquartile range (IQR)0

Descriptive statistics

Standard deviation207.586132
Coefficient of variation (CV)17.88036284
Kurtosis476.7534646
Mean11.60972704
Median Absolute Deviation (MAD)0
Skewness21.07824614
Sum112904.5955
Variance43092.00219
MonotocityNot monotonic
2021-04-16T12:50:17.071590image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram with fixed size bins (bins=36)
ValueCountFrequency (%)
09676
99.5%
58.13918365
 
0.1%
4263.2319815
 
0.1%
1887.0713112
 
< 0.1%
5358.7107872
 
< 0.1%
1588.6589942
 
< 0.1%
5446.3122712
 
< 0.1%
4428.5277732
 
< 0.1%
5448.9266952
 
< 0.1%
1230.0033531
 
< 0.1%
Other values (26)26
 
0.3%
ValueCountFrequency (%)
09676
99.5%
8.7442982191
 
< 0.1%
27.676572881
 
< 0.1%
58.13918365
 
0.1%
501.9017311
 
< 0.1%
ValueCountFrequency (%)
5448.9266952
< 0.1%
5446.3122712
< 0.1%
5358.7107872
< 0.1%
4441.2057821
< 0.1%
4428.5277732
< 0.1%

dt_for_away
Real number (ℝ≥0)

Distinct1534
Distinct (%)15.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1002.414841
Minimum1
Maximum5277.157425
Zeros0
Zeros (%)0.0%
Memory size76.1 KiB
2021-04-16T12:50:17.187641image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile182.5727665
Q1462.1027044
median872.6534573
Q31371.338114
95-th percentile2301.123231
Maximum5277.157425
Range5276.157425
Interquartile range (IQR)909.2354094

Descriptive statistics

Standard deviation664.5236289
Coefficient of variation (CV)0.6629227758
Kurtosis0.312291373
Mean1002.414841
Median Absolute Deviation (MAD)443.2145362
Skewness0.8356479375
Sum9748484.329
Variance441591.6534
MonotocityNot monotonic
2021-04-16T12:50:17.314428image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
171.277677949
 
0.5%
82.6434508549
 
0.5%
286.159470247
 
0.5%
1371.33811446
 
0.5%
170.305288745
 
0.5%
1094.53915945
 
0.5%
258.193156244
 
0.5%
284.968323143
 
0.4%
1234.38861143
 
0.4%
1174.3497143
 
0.4%
Other values (1524)9271
95.3%
ValueCountFrequency (%)
122
0.2%
26.686854372
 
< 0.1%
32.25485441
 
< 0.1%
82.6434508549
0.5%
82.8790351722
0.2%
ValueCountFrequency (%)
5277.1574251
< 0.1%
4850.9940141
< 0.1%
4795.703111
< 0.1%
4628.6532151
< 0.1%
4187.0811331
< 0.1%

compass_away
Categorical

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size76.1 KiB
NE
2967 
NW
2662 
SW
2209 
SE
1887 

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters19450
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowSE
2nd rowNE
3rd rowSE
4th rowNW
5th rowNW
ValueCountFrequency (%)
NE2967
30.5%
NW2662
27.4%
SW2209
22.7%
SE1887
19.4%
2021-04-16T12:50:17.607570image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram of lengths of the category
2021-04-16T12:50:17.668781image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
ValueCountFrequency (%)
ne2967
30.5%
nw2662
27.4%
sw2209
22.7%
se1887
19.4%

Most occurring characters

ValueCountFrequency (%)
N5629
28.9%
W4871
25.0%
E4854
25.0%
S4096
21.1%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter19450
100.0%

Most frequent character per category

ValueCountFrequency (%)
N5629
28.9%
W4871
25.0%
E4854
25.0%
S4096
21.1%

Most occurring scripts

ValueCountFrequency (%)
Latin19450
100.0%

Most frequent character per script

ValueCountFrequency (%)
N5629
28.9%
W4871
25.0%
E4854
25.0%
S4096
21.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII19450
100.0%

Most frequent character per block

ValueCountFrequency (%)
N5629
28.9%
W4871
25.0%
E4854
25.0%
S4096
21.1%

compass_home
Categorical

HIGH CORRELATION
MISSING

Distinct4
Distinct (%)8.2%
Missing9676
Missing (%)99.5%
Memory size76.1 KiB
NE
23 
SE
12 
NW
SW

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters98
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowNE
2nd rowSE
3rd rowNE
4th rowSE
5th rowNW
ValueCountFrequency (%)
NE23
 
0.2%
SE12
 
0.1%
NW8
 
0.1%
SW6
 
0.1%
(Missing)9676
99.5%
2021-04-16T12:50:17.841854image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram of lengths of the category
2021-04-16T12:50:17.901946image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
ValueCountFrequency (%)
ne23
46.9%
se12
24.5%
nw8
 
16.3%
sw6
 
12.2%

Most occurring characters

ValueCountFrequency (%)
E35
35.7%
N31
31.6%
S18
18.4%
W14
 
14.3%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter98
100.0%

Most frequent character per category

ValueCountFrequency (%)
E35
35.7%
N31
31.6%
S18
18.4%
W14
 
14.3%

Most occurring scripts

ValueCountFrequency (%)
Latin98
100.0%

Most frequent character per script

ValueCountFrequency (%)
E35
35.7%
N31
31.6%
S18
18.4%
W14
 
14.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII98
100.0%

Most frequent character per block

ValueCountFrequency (%)
E35
35.7%
N31
31.6%
S18
18.4%
W14
 
14.3%

elo1_pre
Real number (ℝ≥0)

HIGH CORRELATION

Distinct9627
Distinct (%)99.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1508.718489
Minimum1197.301
Maximum1839.663
Zeros0
Zeros (%)0.0%
Memory size76.1 KiB
2021-04-16T12:50:17.990002image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/

Quantile statistics

Minimum1197.301
5-th percentile1342.2866
Q11439.76274
median1508.928
Q31577.621
95-th percentile1670.48353
Maximum1839.663
Range642.362
Interquartile range (IQR)137.8582602

Descriptive statistics

Standard deviation99.0675304
Coefficient of variation (CV)0.0656633634
Kurtosis-0.2879641969
Mean1508.718489
Median Absolute Deviation (MAD)68.854
Skewness-0.01664253106
Sum14672287.3
Variance9814.37558
MonotocityNot monotonic
2021-04-16T12:50:18.116775image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
13003
 
< 0.1%
1524.2912
 
< 0.1%
1485.9032
 
< 0.1%
1600.7012
 
< 0.1%
1584.0242
 
< 0.1%
1495.3032
 
< 0.1%
1617.3832
 
< 0.1%
1456.7392
 
< 0.1%
1425.5472
 
< 0.1%
1419.6982
 
< 0.1%
Other values (9617)9704
99.8%
ValueCountFrequency (%)
1197.3011
< 0.1%
1200.1291
< 0.1%
1219.337611
< 0.1%
1227.4909281
< 0.1%
1228.0561
< 0.1%
ValueCountFrequency (%)
1839.6631
< 0.1%
1831.4621
< 0.1%
1824.2241
< 0.1%
1821.8151
< 0.1%
1810.5021
< 0.1%

elo2_pre
Real number (ℝ≥0)

HIGH CORRELATION

Distinct9612
Distinct (%)98.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1507.068132
Minimum1201.561463
Maximum1825.961
Zeros0
Zeros (%)0.0%
Memory size76.1 KiB
2021-04-16T12:50:18.248559image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/

Quantile statistics

Minimum1201.561463
5-th percentile1343.489676
Q11439.334
median1508.481
Q31576.814
95-th percentile1663.519
Maximum1825.961
Range624.399537
Interquartile range (IQR)137.48

Descriptive statistics

Standard deviation97.18539006
Coefficient of variation (CV)0.06448639449
Kurtosis-0.356238729
Mean1507.068132
Median Absolute Deviation (MAD)68.688
Skewness-0.07509450543
Sum14656237.58
Variance9445.00004
MonotocityNot monotonic
2021-04-16T12:50:18.374872image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1552.3693
 
< 0.1%
1488.0233
 
< 0.1%
1510.3592
 
< 0.1%
1503.5172
 
< 0.1%
1411.3172
 
< 0.1%
1513.7322
 
< 0.1%
1512.7072
 
< 0.1%
1484.4222
 
< 0.1%
1561.4952
 
< 0.1%
1441.3022
 
< 0.1%
Other values (9602)9703
99.8%
ValueCountFrequency (%)
1201.5614631
< 0.1%
1210.7738861
< 0.1%
1212.0181
< 0.1%
1214.9261
< 0.1%
1226.0281
< 0.1%
ValueCountFrequency (%)
1825.9611
< 0.1%
1809.4391
< 0.1%
1806.3461
< 0.1%
1785.3921
< 0.1%
1783.6351
< 0.1%

elo_prob1
Real number (ℝ≥0)

HIGH CORRELATION

Distinct9674
Distinct (%)99.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.5836280748
Minimum0.0709532918
Maximum0.9645780571
Zeros0
Zeros (%)0.0%
Memory size76.1 KiB
2021-04-16T12:50:18.510437image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/

Quantile statistics

Minimum0.0709532918
5-th percentile0.28779041
Q10.4666119068
median0.594092781
Q30.7102463049
95-th percentile0.8402533518
Maximum0.9645780571
Range0.8936247653
Interquartile range (IQR)0.2436343981

Descriptive statistics

Standard deviation0.167347844
Coefficient of variation (CV)0.2867371382
Kurtosis-0.5588378845
Mean0.5836280748
Median Absolute Deviation (MAD)0.121384362
Skewness-0.2727265523
Sum5675.783027
Variance0.02800530088
MonotocityNot monotonic
2021-04-16T12:50:18.638651image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.53551646242
 
< 0.1%
0.58559367392
 
< 0.1%
0.53164123152
 
< 0.1%
0.58517592682
 
< 0.1%
0.43939747082
 
< 0.1%
0.62582869852
 
< 0.1%
0.44020587612
 
< 0.1%
0.32457225682
 
< 0.1%
0.5439091472
 
< 0.1%
0.59845097332
 
< 0.1%
Other values (9664)9705
99.8%
ValueCountFrequency (%)
0.07095329181
< 0.1%
0.10632428181
< 0.1%
0.10750878421
< 0.1%
0.11144128951
< 0.1%
0.11409570761
< 0.1%
ValueCountFrequency (%)
0.96457805711
< 0.1%
0.95656120561
< 0.1%
0.95425416451
< 0.1%
0.95394386461
< 0.1%
0.94728713291
< 0.1%

elo_prob2
Real number (ℝ≥0)

HIGH CORRELATION

Distinct9674
Distinct (%)99.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.4163719252
Minimum0.03542194285
Maximum0.9290467082
Zeros0
Zeros (%)0.0%
Memory size76.1 KiB
2021-04-16T12:50:18.772287image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/

Quantile statistics

Minimum0.03542194285
5-th percentile0.1597466482
Q10.2897536951
median0.405907219
Q30.5333880932
95-th percentile0.71220959
Maximum0.9290467082
Range0.8936247653
Interquartile range (IQR)0.2436343981

Descriptive statistics

Standard deviation0.167347844
Coefficient of variation (CV)0.4019191348
Kurtosis-0.5588378845
Mean0.4163719252
Median Absolute Deviation (MAD)0.121384362
Skewness0.2727265523
Sum4049.216973
Variance0.02800530088
MonotocityNot monotonic
2021-04-16T12:50:18.899090image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.67542774322
 
< 0.1%
0.43547780292
 
< 0.1%
0.542615072
 
< 0.1%
0.57150540842
 
< 0.1%
0.46944109672
 
< 0.1%
0.14258562972
 
< 0.1%
0.46835876852
 
< 0.1%
0.36172064552
 
< 0.1%
0.46448353762
 
< 0.1%
0.53116389632
 
< 0.1%
Other values (9664)9705
99.8%
ValueCountFrequency (%)
0.035421942851
< 0.1%
0.043438794421
< 0.1%
0.04574583551
< 0.1%
0.046056135371
< 0.1%
0.052712867071
< 0.1%
ValueCountFrequency (%)
0.92904670821
< 0.1%
0.89367571821
< 0.1%
0.89249121581
< 0.1%
0.88855871051
< 0.1%
0.88590429241
< 0.1%

qbelo1_pre
Real number (ℝ≥0)

HIGH CORRELATION
UNIQUE

Distinct9725
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1507.91921
Minimum1198.229025
Maximum1806.39016
Zeros0
Zeros (%)0.0%
Memory size76.1 KiB
2021-04-16T12:50:19.030516image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/

Quantile statistics

Minimum1198.229025
5-th percentile1347.139155
Q11441.815237
median1509.836322
Q31573.636654
95-th percentile1664.540825
Maximum1806.39016
Range608.1611351
Interquartile range (IQR)131.8214173

Descriptive statistics

Standard deviation95.54391819
Coefficient of variation (CV)0.06336143047
Kurtosis-0.2821861756
Mean1507.91921
Median Absolute Deviation (MAD)65.92825766
Skewness-0.04901712861
Sum14664514.32
Variance9128.640303
MonotocityNot monotonic
2021-04-16T12:50:19.153762image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1494.9241211
 
< 0.1%
1475.520481
 
< 0.1%
1393.5389491
 
< 0.1%
1453.756841
 
< 0.1%
1552.1619721
 
< 0.1%
1426.5686521
 
< 0.1%
1457.0574861
 
< 0.1%
1375.9248411
 
< 0.1%
1456.7384471
 
< 0.1%
1484.6927711
 
< 0.1%
Other values (9715)9715
99.9%
ValueCountFrequency (%)
1198.2290251
< 0.1%
1200.5849231
< 0.1%
1226.7468781
< 0.1%
1228.2609811
< 0.1%
1229.6751021
< 0.1%
ValueCountFrequency (%)
1806.390161
< 0.1%
1800.2565921
< 0.1%
1793.9137211
< 0.1%
1792.0622231
< 0.1%
1789.8741631
< 0.1%

qbelo2_pre
Real number (ℝ≥0)

HIGH CORRELATION
UNIQUE

Distinct9725
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1506.738767
Minimum1206.174113
Maximum1798.835806
Zeros0
Zeros (%)0.0%
Memory size76.1 KiB
2021-04-16T12:50:19.382032image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/

Quantile statistics

Minimum1206.174113
5-th percentile1347.798055
Q11441.904273
median1509.452957
Q31573.941991
95-th percentile1657.167627
Maximum1798.835806
Range592.6616936
Interquartile range (IQR)132.0377187

Descriptive statistics

Standard deviation93.71171232
Coefficient of variation (CV)0.06219506286
Kurtosis-0.3300392236
Mean1506.738767
Median Absolute Deviation (MAD)65.97244765
Skewness-0.1080958958
Sum14653034.51
Variance8781.885026
MonotocityNot monotonic
2021-04-16T12:50:19.507131image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1610.3455551
 
< 0.1%
1655.5958541
 
< 0.1%
1372.3569121
 
< 0.1%
1458.1497511
 
< 0.1%
1496.6033791
 
< 0.1%
1532.436371
 
< 0.1%
1492.2251561
 
< 0.1%
1374.312931
 
< 0.1%
1541.7379081
 
< 0.1%
1532.4434561
 
< 0.1%
Other values (9715)9715
99.9%
ValueCountFrequency (%)
1206.1741131
< 0.1%
1210.9042011
< 0.1%
1213.2035831
< 0.1%
1220.8380481
< 0.1%
1223.6966431
< 0.1%
ValueCountFrequency (%)
1798.8358061
< 0.1%
1795.5140491
< 0.1%
1783.2193491
< 0.1%
1783.0446491
< 0.1%
1779.0112931
< 0.1%

qb1_value_pre
Real number (ℝ)

Distinct9671
Distinct (%)99.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean104.5766828
Minimum-53.77891723
Maximum317.472758
Zeros33
Zeros (%)0.3%
Memory size76.1 KiB
2021-04-16T12:50:19.643297image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/

Quantile statistics

Minimum-53.77891723
5-th percentile19.93142791
Q160.39579427
median98.42855351
Q3142.2045079
95-th percentile213.3488834
Maximum317.472758
Range371.2516753
Interquartile range (IQR)81.80871365

Descriptive statistics

Standard deviation58.95798196
Coefficient of variation (CV)0.5637775114
Kurtosis-0.01468244333
Mean104.5766828
Median Absolute Deviation (MAD)40.5139525
Skewness0.5042780088
Sum1017008.24
Variance3476.043637
MonotocityNot monotonic
2021-04-16T12:50:19.769501image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
033
 
0.3%
112.901587
 
0.1%
103.379764
 
< 0.1%
42.0773762
 
< 0.1%
66.199322
 
< 0.1%
32.646242
 
< 0.1%
16.323122
 
< 0.1%
28.112042
 
< 0.1%
52.14332
 
< 0.1%
112.448162
 
< 0.1%
Other values (9661)9667
99.4%
ValueCountFrequency (%)
-53.778917231
< 0.1%
-44.98544371
< 0.1%
-39.036960521
< 0.1%
-38.106327041
< 0.1%
-35.867069081
< 0.1%
ValueCountFrequency (%)
317.4727581
< 0.1%
313.82838351
< 0.1%
308.7623811
< 0.1%
306.84413241
< 0.1%
305.25446191
< 0.1%

qb2_value_pre
Real number (ℝ)

Distinct9690
Distinct (%)99.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean104.4098118
Minimum-45.31072334
Maximum327.7165449
Zeros26
Zeros (%)0.3%
Memory size76.1 KiB
2021-04-16T12:50:19.903183image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/

Quantile statistics

Minimum-45.31072334
5-th percentile20.31476558
Q161.02975275
median97.59695147
Q3141.4013777
95-th percentile212.9138725
Maximum327.7165449
Range373.0272682
Interquartile range (IQR)80.37162497

Descriptive statistics

Standard deviation58.4171454
Coefficient of variation (CV)0.5594986179
Kurtosis-0.02450860585
Mean104.4098118
Median Absolute Deviation (MAD)39.79824715
Skewness0.504049428
Sum1015385.42
Variance3412.562877
MonotocityNot monotonic
2021-04-16T12:50:20.028540image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
026
 
0.3%
112.901585
 
0.1%
23.124422
 
< 0.1%
108.82082
 
< 0.1%
74.81432
 
< 0.1%
61.289429142
 
< 0.1%
111.994742
 
< 0.1%
68.0132
 
< 0.1%
99.260917651
 
< 0.1%
86.224069881
 
< 0.1%
Other values (9680)9680
99.5%
ValueCountFrequency (%)
-45.310723341
< 0.1%
-36.567947131
< 0.1%
-34.949901641
< 0.1%
-33.932203311
< 0.1%
-29.90721051
< 0.1%
ValueCountFrequency (%)
327.71654491
< 0.1%
310.1306781
< 0.1%
307.03426451
< 0.1%
300.76185811
< 0.1%
300.23408331
< 0.1%

qbelo_prob1
Real number (ℝ≥0)

HIGH CORRELATION
UNIQUE

Distinct9725
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.5759398122
Minimum0.05981049406
Maximum0.9671966037
Zeros0
Zeros (%)0.0%
Memory size76.1 KiB
2021-04-16T12:50:20.164392image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/

Quantile statistics

Minimum0.05981049406
5-th percentile0.2759039736
Q10.4540434789
median0.5874577138
Q30.7083993485
95-th percentile0.8397586078
Maximum0.9671966037
Range0.9073861096
Interquartile range (IQR)0.2543558696

Descriptive statistics

Standard deviation0.1721991186
Coefficient of variation (CV)0.2989880452
Kurtosis-0.5921599876
Mean0.5759398122
Median Absolute Deviation (MAD)0.1273096889
Skewness-0.2569352478
Sum5601.014674
Variance0.02965253645
MonotocityNot monotonic
2021-04-16T12:50:20.291708image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.61533884411
 
< 0.1%
0.5111208991
 
< 0.1%
0.73773042751
 
< 0.1%
0.59439835351
 
< 0.1%
0.66972782971
 
< 0.1%
0.51116901561
 
< 0.1%
0.86360311181
 
< 0.1%
0.60508965461
 
< 0.1%
0.75947104521
 
< 0.1%
0.851676851
 
< 0.1%
Other values (9715)9715
99.9%
ValueCountFrequency (%)
0.059810494061
< 0.1%
0.070228847641
< 0.1%
0.086213926911
< 0.1%
0.10057017131
< 0.1%
0.10070933281
< 0.1%
ValueCountFrequency (%)
0.96719660371
< 0.1%
0.96590984211
< 0.1%
0.95657387441
< 0.1%
0.95226836461
< 0.1%
0.94839176891
< 0.1%

qbelo_prob2
Real number (ℝ≥0)

HIGH CORRELATION
UNIQUE

Distinct9725
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.4240601878
Minimum0.03280339633
Maximum0.9401895059
Zeros0
Zeros (%)0.0%
Memory size76.1 KiB
2021-04-16T12:50:20.426180image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/

Quantile statistics

Minimum0.03280339633
5-th percentile0.1602413922
Q10.2916006515
median0.4125422862
Q30.5459565211
95-th percentile0.7240960264
Maximum0.9401895059
Range0.9073861096
Interquartile range (IQR)0.2543558696

Descriptive statistics

Standard deviation0.1721991186
Coefficient of variation (CV)0.4060723538
Kurtosis-0.5921599876
Mean0.4240601878
Median Absolute Deviation (MAD)0.1273096889
Skewness0.2569352478
Sum4123.985326
Variance0.02965253645
MonotocityNot monotonic
2021-04-16T12:50:20.555918image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.76682454391
 
< 0.1%
0.41241506631
 
< 0.1%
0.3091691771
 
< 0.1%
0.22754658521
 
< 0.1%
0.66908004741
 
< 0.1%
0.439376451
 
< 0.1%
0.36650857861
 
< 0.1%
0.37818796591
 
< 0.1%
0.40307777441
 
< 0.1%
0.2723251961
 
< 0.1%
Other values (9715)9715
99.9%
ValueCountFrequency (%)
0.032803396331
< 0.1%
0.034090157931
< 0.1%
0.043426125611
< 0.1%
0.04773163541
< 0.1%
0.051608231111
< 0.1%
ValueCountFrequency (%)
0.94018950591
< 0.1%
0.92977115241
< 0.1%
0.91378607311
< 0.1%
0.89942982871
< 0.1%
0.89929066721
< 0.1%

home_fav
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size76.1 KiB
1
6506 
0
3219 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters9725
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row0
3rd row1
4th row1
5th row1
ValueCountFrequency (%)
16506
66.9%
03219
33.1%
2021-04-16T12:50:20.760042image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram of lengths of the category
2021-04-16T12:50:20.819061image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
ValueCountFrequency (%)
16506
66.9%
03219
33.1%

Most occurring characters

ValueCountFrequency (%)
16506
66.9%
03219
33.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number9725
100.0%

Most frequent character per category

ValueCountFrequency (%)
16506
66.9%
03219
33.1%

Most occurring scripts

ValueCountFrequency (%)
Common9725
100.0%

Most frequent character per script

ValueCountFrequency (%)
16506
66.9%
03219
33.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII9725
100.0%

Most frequent character per block

ValueCountFrequency (%)
16506
66.9%
03219
33.1%

Interactions

2021-04-16T12:49:55.673427image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-16T12:49:55.782198image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-16T12:49:55.883671image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-16T12:49:55.983384image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-16T12:49:56.170992image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-16T12:49:56.273835image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-16T12:49:56.370661image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-16T12:49:56.465955image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-16T12:49:56.568034image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-16T12:49:56.669848image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-16T12:49:56.774131image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-16T12:49:56.878769image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-16T12:49:56.977840image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-16T12:49:57.074311image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-16T12:49:57.178649image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-16T12:49:57.284883image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-16T12:49:57.385271image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-16T12:49:57.487271image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-16T12:49:57.589700image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-16T12:49:57.687246image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-16T12:49:57.783745image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-16T12:49:57.884786image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-16T12:49:57.987418image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-16T12:49:58.091528image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-16T12:49:58.194302image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-16T12:49:58.290881image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-16T12:49:58.389015image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-16T12:49:58.582443image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-16T12:49:58.681383image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-16T12:49:58.781790image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-16T12:49:58.882329image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-16T12:49:58.982719image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-16T12:49:59.079310image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-16T12:49:59.177390image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-16T12:49:59.280785image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-16T12:49:59.382257image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-16T12:49:59.485454image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-16T12:49:59.589314image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-16T12:49:59.687790image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-16T12:49:59.790181image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-16T12:49:59.891549image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-16T12:49:59.990356image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-16T12:50:00.088052image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-16T12:50:00.189953image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-16T12:50:00.291165image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-16T12:50:00.390004image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-16T12:50:00.489518image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-16T12:50:00.591619image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-16T12:50:00.691736image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-16T12:50:00.793945image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-16T12:50:00.996141image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-16T12:50:01.092840image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-16T12:50:01.191315image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-16T12:50:01.295992image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-16T12:50:01.398844image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-16T12:50:01.502313image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-16T12:50:01.610073image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-16T12:50:01.721659image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-16T12:50:01.826120image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-16T12:50:01.929693image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-16T12:50:02.034866image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-16T12:50:02.140086image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-16T12:50:02.248780image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-16T12:50:02.357614image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-16T12:50:02.459227image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-16T12:50:02.560748image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-16T12:50:02.661669image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-16T12:50:02.763738image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-16T12:50:02.867433image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-16T12:50:02.970901image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-16T12:50:03.076989image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-16T12:50:03.179028image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-16T12:50:03.280738image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-16T12:50:03.482858image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-16T12:50:03.592417image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-16T12:50:03.700411image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-16T12:50:03.806685image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-16T12:50:03.910074image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-16T12:50:04.012312image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-16T12:50:04.108867image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-16T12:50:04.205126image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-16T12:50:04.300671image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-16T12:50:04.398502image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-16T12:50:04.496904image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-16T12:50:04.597234image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-16T12:50:04.692910image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-16T12:50:04.791517image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-16T12:50:04.890635image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-16T12:50:04.993881image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-16T12:50:05.093627image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-16T12:50:05.187941image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-16T12:50:05.284069image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-16T12:50:05.381712image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-16T12:50:05.477343image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-16T12:50:05.574436image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-16T12:50:05.669247image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-16T12:50:05.866239image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-16T12:50:05.966824image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-16T12:50:06.063031image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-16T12:50:06.161038image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-16T12:50:06.259932image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-16T12:50:06.360900image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-16T12:50:06.461208image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-16T12:50:06.556242image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-16T12:50:06.650146image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-16T12:50:06.753256image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-16T12:50:06.856297image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-16T12:50:06.960975image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-16T12:50:07.064761image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-16T12:50:07.168572image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-16T12:50:07.273532image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-16T12:50:07.378476image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-16T12:50:07.483127image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-16T12:50:07.592210image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-16T12:50:07.698790image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-16T12:50:07.805772image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-16T12:50:07.914474image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-16T12:50:08.019998image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-16T12:50:08.126303image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-16T12:50:08.334334image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-16T12:50:08.437483image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-16T12:50:08.539736image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-16T12:50:08.647164image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-16T12:50:08.751583image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-16T12:50:08.852501image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-16T12:50:08.953576image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-16T12:50:09.059867image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-16T12:50:09.166185image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-16T12:50:09.274352image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-16T12:50:09.376363image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-16T12:50:09.476548image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-16T12:50:09.583248image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-16T12:50:09.689301image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-16T12:50:09.795190image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-16T12:50:09.898212image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-16T12:50:10.006000image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-16T12:50:10.114356image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-16T12:50:10.217810image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-16T12:50:10.321849image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-16T12:50:10.430773image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-16T12:50:10.538517image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-16T12:50:10.745386image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-16T12:50:10.847772image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-16T12:50:10.951046image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-16T12:50:11.055246image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-16T12:50:11.161275image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-16T12:50:11.268131image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-16T12:50:11.374930image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-16T12:50:11.484283image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-16T12:50:11.592117image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-16T12:50:11.695947image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-16T12:50:11.798811image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-16T12:50:11.911122image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-16T12:50:12.024015image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-16T12:50:12.134866image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-16T12:50:12.239661image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-16T12:50:12.344692image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-16T12:50:12.445135image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-16T12:50:12.541584image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-16T12:50:12.639201image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-16T12:50:12.736542image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-16T12:50:12.835742image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-16T12:50:12.934725image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-16T12:50:13.028802image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-16T12:50:13.227899image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-16T12:50:13.326794image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-16T12:50:13.429176image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-16T12:50:13.532051image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-16T12:50:13.633648image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-16T12:50:13.731573image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-16T12:50:13.828686image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-16T12:50:13.926389image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-16T12:50:14.023327image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-16T12:50:14.123708image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-16T12:50:14.225673image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-16T12:50:14.325737image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-16T12:50:14.420262image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-16T12:50:14.515065image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-16T12:50:14.614130image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-16T12:50:14.714336image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-16T12:50:14.815515image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-16T12:50:14.919997image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/

Correlations

2021-04-16T12:50:20.898728image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2021-04-16T12:50:21.194651image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2021-04-16T12:50:21.398170image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2021-04-16T12:50:21.609622image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.
2021-04-16T12:50:21.806999image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/

Cramér's V (φc)

Cramér's V is an association measure for nominal random variables. The coefficient ranges from 0 to 1, with 0 indicating independence and 1 indicating perfect association. The empirical estimators used for Cramér's V have been proved to be biased, even for large samples. We use a bias-corrected measure that has been proposed by Bergsma in 2013 that can be found here.

Missing values

2021-04-16T12:50:15.149493image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
A simple visualization of nullity by column.
2021-04-16T12:50:15.501052image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2021-04-16T12:50:15.753635image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
The dendrogram allows you to more fully correlate variable completion, revealing trends deeper than the pairwise ones visible in the correlation heatmap.

Sample

First rows

df_indexresultspread_favoriteover_under_linestadium_neutraldt_for_homedt_for_awaycompass_awaycompass_homeelo1_preelo2_preelo_prob1elo_prob2qbelo1_preqbelo2_preqb1_value_preqb2_value_preqbelo_prob1qbelo_prob2home_fav
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122990-5.039False0.01174.349710NENaN1422.8201573.5130.3791190.6208811423.2393531559.95052258.06348596.1223450.3753530.6246470
223001-3.031False0.0184.036793SENaN1485.8061462.5020.6244120.3755881483.9094901464.01976842.75679141.4946680.6083960.3916041
323011-3.031.5False0.01095.723761NWNaN1579.1211491.3800.7066660.2933341580.3486261487.02432721.70088890.9294080.6756020.3243981
423021-1.037False0.0958.341925NWNaN1408.0821420.1600.5755770.4244231411.6477421424.21090249.196730121.4144960.5280070.4719931
523030-4.036.5False0.0369.669204SENaN1571.8371543.1080.6317070.3682931570.5303501554.74634779.88982839.5395310.6175160.3824841
623041-7.032False0.01584.373778NENaN1501.0701376.8950.7481890.2518111501.3926971385.55690116.15214514.9506340.7075200.2924801
723050-5.032False0.0424.760553SWNaN1454.5411472.7480.5669360.4330641447.1595451464.242458135.57509457.5117630.5631440.4368561
823060-2.041False0.0400.081904SENaN1486.7031483.7340.5965860.4034141491.2734431479.16006232.187388118.0722330.5789640.4210361
923071-7.031.5False0.082.958831SWNaN1516.2851435.8620.6978590.3021411515.4011981430.89280568.93902947.9364080.6862890.3137111

Last rows

df_indexresultspread_favoriteover_under_linestadium_neutraldt_for_homedt_for_awaycompass_awaycompass_homeelo1_preelo2_preelo_prob1elo_prob2qbelo1_preqbelo2_preqb1_value_preqb2_value_preqbelo_prob1qbelo_prob2home_fav
9715120131-11.048False0.0833.524038SWNaN1695.6835991500.1184650.8175650.1824351730.5346381497.160456222.286925164.8393310.8498200.1501801
9716120140-5.547.5False0.0114.228939SENaN1572.1614421516.9817690.6663690.3336311578.4457221536.927531204.930263164.4893120.6194430.3805571
9717120150-3.553.5False0.0596.492606SWNaN1599.0765991654.2150040.5141880.4858121571.6963931651.052726216.955557249.5656730.4257140.5742860
9718120161-2.549.5False0.0265.725548NWNaN1700.5380091675.6957770.6264870.3735131688.2529861668.348824289.086698243.1852250.6528860.3471141
9719120171-7.045False0.01758.300526NENaN1700.2260621620.4985350.6970140.3029861674.7860931633.700632266.649955161.6745290.7108750.2891251
9720120181-8.056False0.0696.123935SWNaN1712.6520901552.0127250.7856470.2143531711.2170291568.944343273.850726182.7177660.7899270.2100731
9721120190-2.553False0.0481.270463NWNaN1704.0512941645.0740080.6712120.3287881737.3112961624.424406226.770860221.7779060.7058590.2941411
9722120200-3.053False0.01198.450281NWNaN1715.6231871679.1862550.6419680.3580321689.4067921660.789489276.653962221.7470910.6291910.3708091
9723120211-3.055False0.0856.619992SWNaN1719.6189211719.9741490.5919720.4080281718.0322471706.159766271.760879273.2900460.5356370.4643631
9724120221-3.056False0.01033.075041SENaN1703.3032961741.0872790.5390870.4609131684.3190581742.902172211.680227282.2613260.4662060.5337940